Efficient crack detection and quantification in concrete structures using IoT

Nair, Ajay; Hemalatha, R.; Sangeetha, P.; K, Harish Kumar; P, Dinesh Kumar; Sahith, Inakota Sai; Radha, S.

doi:10.1080/1448837x.2021.1914905

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…64,80 Xu et al 83 addressed this limitation by modifying the conventional TR method. They defined tightness indices based on the phase shift 202 Aluminum Sheet ThingWorx Cross-correlation index Damage location and width Chowdhry et al 203 Excitation motor ThingSpeak Frequency response Fault diagnosis Harshitha et al 204 Aluminum Beam ThingSpeak Cross-correlation index Damage detection 208 Concrete structures RaspberryPi camera CNN Specimen cracks Wang et al 209 Fresh concrete Camera sensor CNN Vibration quality and signal amplitude obtained from the focused signal. They concluded that the monotonous trend of their method's tightness indices was better in estimating early bolt looseness in comparison to WED and TR methods.…”

Section: Discussion and Future Scopementioning

confidence: 99%

“…In addition, the fusion of ML algorithms with these IoT systems enhanced the damage recognition ability. 208 Zhang et al 205 developed a bridge monitoring system based on IoT technology for crack identification. They incorporated the CNN algorithm in their IoT framework for accurate bridge diagnosis.…”

Section: Discussion and Future Scopementioning

confidence: 99%

“…They demonstrated the efficacy of their approach by comparing it with the existing approaches. Nair et al 208 developed an efficient crack detection system based on ML and IoT. They segmented their model into three steps: (i) image processing, (ii) IoT-based monitoring, and (iii) crack analysis.…”

Section: Iot-based Shm Of Structures Using ML Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

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The integrity of bolted joints is still a challenging problem owing to the gross or localized slip at the interfacial surfaces of the joints when subjected to external disturbances such as vibrations. This slip escalates the interfacial movement and, thus, leads to a decrease in preload levels, that is, looseness of the bolted assemblies. In the last decade, modal analysis, wave propagation, and percussion methods were traditionally used to detect looseness in bolted connections. With an increase in computational power, machine learning algorithms such as neural networks, random forests, decision trees, and support vector machines complemented the traditional methods in accurate looseness estimation. Subsequently, this integration paved the path for real-time health monitoring of bolted joints. This paper summarizes recent investigations on looseness detection in bolted assemblies based on traditional methods and machine learning algorithms. The working principle, advantages, challenges, and applications of the aforementioned methods are also detailed in this paper. Apart from these investigations, the latest studies on the Internet of Things-based health monitoring of structures are also reviewed to explore their adaptability in remote monitoring of bolted connections for damage detection in the future.

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Section: Discussion and Future Scopementioning

confidence: 99%

Section: Discussion and Future Scopementioning

confidence: 99%

See 1 more Smart Citation

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…(11), cracks in concrete bridges were identified by semantic segmentation of images acquired by mobile devices. (12), concrete structures were subjected to load testing, and cracks in the structures were detected by morphological processing: a Gaussian filter was applied followed by the Bottom hat transform to detect detail elements that were subsequently segmented using the Otsu method. Other techniques such as Wavelet Scattering Transform (WST) have been used for pattern recognition in texture discrimination to obtain irises information (13).…”

Section: Introductionmentioning

confidence: 99%

Técnicas de visión artificial y aprendizaje de máquinas para la detección y clasificación de grietas

Tello-Cifuentes

Marulanda

Thomson

2023

inycomp

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En los procesos de mantenimiento y rehabilitación de vías, es importante el desarrollo de procedimientos que contribuyan a la evaluación de la condición del pavimento. Los métodos de inspección de la superficie de pavimento que emplean imágenes capturan información permitiendo un análisis cuantitativo. Este documento presenta una metodología para la detección de grietas en el pavimento, mediante la aplicación de técnicas de procesamientos de imágenes y redes neuronales artificiales; está dividido en cuatro etapas: 1. Adquisición de las imágenes, 2. Procesamiento de imágenes, iii. Extracción de características y iv. Clasificación utilizando RNA. La metodología se aplicó para la detección de los deterioros: grieta longitudinal, bache y piel de cocodrilo. La clasificación se realizó mediante una red neuronal MLP con configuración (12 14 3), la cual obtuvo una exactitud de 95,56% y una precisión de 94,44%. La metodología propuesta puede ser útil para las organizaciones gubernamentales en la evaluación de la malla vial.

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Use of IoT for structural health monitoring of civil engineering structures: a state-of-the-art review

Bhatta,

Dang

2024

Urban Lifeline

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The primary goal of Structural Health Monitoring (SHM) is to assess performance and determine the physical state of the structures. Technological improvements and the ubiquitous accessibility of Wi-Fi networks have enhanced real-time SHM based on the Internet of Things (IoT). Extensive structural health evaluation can be conducted using real-time test data collected from various IoT sensors on civil infrastructures. These sensors monitor multiple structural health parameters, and the data is accessible via cloud-based storage platforms. This paper presents an overview of IoT technologies and provides an extensive literature review of IoT applications for civil infrastructures, highlighting associated challenges.

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Efficient crack detection and quantification in concrete structures using IoT

Cited by 4 publications

References 16 publications

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Técnicas de visión artificial y aprendizaje de máquinas para la detección y clasificación de grietas

Use of IoT for structural health monitoring of civil engineering structures: a state-of-the-art review

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